ClusterM: a scalable algorithm for computational prediction of conserved protein complexes across multiple protein inter
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RESEARCH
Open Access
ClusterM: a scalable algorithm for computational prediction of conserved protein complexes across multiple protein interaction networks Yijie Wang1† , Hyundoo Jeong2† , Byung-Jun Yoon3,4,5 and Xiaoning Qian3,4* From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18–20 August 2020
Abstract Background: The current computational methods on identifying conserved protein complexes across multiple Protein-Protein Interaction (PPI) networks suffer from the lack of explicit modeling of the desired topological properties within conserved protein complexes as well as their scalability. Results: To overcome those issues, we propose a scalable algorithm—ClusterM—for identifying conserved protein complexes across multiple PPI networks through the integration of network topology and protein sequence similarity information. ClusterM overcomes the computational barrier that existed in previous methods, where the complexity escalates exponentially when handling an increasing number of PPI networks; and it is able to detect conserved protein complexes with both topological separability and cohesive protein sequence conservation. On two independent compendiums of PPI networks from Saccharomyces cerevisiae (Sce, yeast), Drosophila melanogaster (Dme, fruit fly), Caenorhabditis elegans (Cel, worm), and Homo sapiens (Hsa, human), we demonstrate that ClusterM outperforms other state-of-the-art algorithms by a significant margin and is able to identify de novo conserved protein complexes across four species that are missed by existing algorithms. Conclusions: ClusterM can better capture the desired topological property of a typical conserved protein complex, which is densely connected within the complex while being well-separated from the rest of the networks. Furthermore, our experiments have shown that ClusterM is highly scalable and efficient when analyzing multiple PPI networks. Keywords: Comparative network analysis, Multiple network alignment and clustering, Conserved module identification
*Correspondence: [email protected] † Yijie Wang and Hyundoo Jeong contributed equally to this work. 3 Department of Electrical and Computer Engineering, Texas A&M University, College Station 77843, TX, USA 4 TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), Texas A&M University, College Station 77843, TX, USA Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative
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